Offline solution based policies for on-call personnel scheduling

About 1.5% of workers in North America who are involved in on-call employment suffer from issues like irregular hours, and lack of flexibility. We present a novel on-call personnel scheduling system and aim to optimize its operation with a dynamic, data-driven approach to alleviate some of these issues. Like traditional systems, it contacts on-call personnel in order of seniority to inform them about the availability of on-call work. However, flexibility in the system allows employees time to think. It also allows senior employees to replace or bump junior employees from their preferred shift if a junior employee had responded early and selected that shift. The replacements are undesirable and the management seeks a policy to make calls to employees to schedule all available shifts while ensuring minimum bumps under uncertain employee response times. We show that this problem is NP-complete even in the case of perfect information. Then, we design easy-to-implement policy approximations based on a threshold structure for the dynamic problem and tune them using offline solutions obtained from simulated data, where all uncertainty is known beforehand. The offline solution-based policies are tested on real-world data and outperform the current policy used by our industrial partner.